code: def cost_and_grad(
      self,
      label_acts: torch.Tensor,
      duration_acts: torch.Tensor,
      label_grads: torch.Tensor,
      duration_grads: torch.Tensor,
      costs: torch.Tensor,
      pad_labels: torch.Tensor,
      label_lengths: torch.Tensor,
      input_lengths: torch.Tensor,
  ):
      if (
          duration_acts is None
          or label_acts is None
          or label_grads is None
          or duration_grads is None
          or costs is None
          or pad_labels is None
          or label_lengths is None
          or input_lengths is None
      ):
          return global_constants.RNNTStatus.RNNT_STATUS_INVALID_VALUE
  
      return self.compute_cost_and_score(
          label_acts,
          duration_acts,
          label_grads,
          duration_grads,
          costs,
          pad_labels,
          label_lengths,
          input_lengths,
      )
  
imports: import multiprocessing
  import random
  from typing import Optional, Tuple
  import numba
  import torch
  from numba import cuda
  from nemo.collections.asr.parts.numba.rnnt_loss.utils import global_constants, rnnt_helper
  from nemo.collections.asr.parts.numba.rnnt_loss.utils.cuda_utils import gpu_rnnt_kernel, reduce
label_desc: Use image node_id 3 for calling the GPUTDT obj's underlying member method code with example usage: obj.cost_and_grad(label_acts, duration_acts, label_grads, duration_grads, costs, pad_labels, label_lengths, input_lengths) and returns: self, global_constants